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1. Matrix-Factorization-for-Recommendation Matrix-Factorization-for-Recommendation Public

   Using Matrix Factorization/Probabilistic Matrix Factorization to solve Recommendation。矩阵分解进行推荐系统算法。

   R 2

2. Matrix-Factorization-Implicit-Feedback Matrix-Factorization-Implicit-Feedback Public

   使用矩阵分解算法处理隐式反馈数据，并进行Top-N推荐。The matrix factorization algorithm is used to process the implicit feedback data and make top-N recommendation.

   2

3. NCF-MF-for-Recommendation NCF-MF-for-Recommendation Public

   分别使用传统方法（KNN，SVD，NMF等）和深度方法（NCF）进行推荐系统的评分预测。Traditional methods (KNN, SVD, NMF, etc.) and depth method (NCF) were used to predict rating of the recommendation system.

   Jupyter Notebook 6

4. P300-BCI-Data-Analysis P300-BCI-Data-Analysis Public

   2020年研究生数学建模竞赛C题，全国二等奖，分析脑机接口数据进行分析预测。The data of BCI were analyzed and predicted.

   Jupyter Notebook 7 3

5. multi-factor-strategy-joinquant multi-factor-strategy-joinquant Public

   在聚宽（joinquant）平台上使用多因子策略进行量化投资模拟。

   Jupyter Notebook 30 9

6. CVPR-2020-LEAP CVPR-2020-LEAP Public

   Unofficial implement of LEAP(Deep Representation Learning on Long-tailed Data: A Learnable Embedding Augmentation Perspective) for Multi-Label Classification.

   Python 9 1